Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

article · 2001

Efficient Data Mining: Scripting and Scalable Parallel Algorithms

Peter Christen, Markus Hegland, Ole M. Nielsen⋆, Stephen Roberts, Peter Strazdins, Tatiana Semenova, Irfan Altas

Cited by 0Abstract and details only

Abstract

This paper presents our approach to data mining that allows the coupling of parallel applications with a scripting language resulting in an efficient and flexible toolbox. Parallel algorithms which are scalable both in data size and number of processors are a key issue to be able to solve the ever increasing problems in data mining. On the other hand, data mining applications should be flexible to allow interactive data exploration. By using a toolbox written in a scripting language we are able to steer parallel applications in a flexible way, thus fulfilling the needs of a data miner for fast interactive data analysis. The chosen approach is discussed and first results are presented.

Cite this paper

APA 7

Christen, P., Hegland, M., Nielsen⋆, O. M., Roberts, S., Strazdins, P., Semenova, T., & Altas, I. (2001). Efficient Data Mining: Scripting and Scalable Parallel Algorithms.

MLA 9

Christen, Peter, et al. “Efficient Data Mining: Scripting and Scalable Parallel Algorithms.” 2001.

Chicago

Christen, Peter, Markus Hegland, Ole M. Nielsen⋆, Stephen Roberts, Peter Strazdins, Tatiana Semenova, and Irfan Altas. 2001. “Efficient Data Mining: Scripting and Scalable Parallel Algorithms.”

Harvard

Christen, P. et al. (2001) ‘Efficient Data Mining: Scripting and Scalable Parallel Algorithms’.

BibTeX
@article{christen2001efficient,
  title = {{Efficient Data Mining: Scripting and Scalable Parallel Algorithms}},
  author = {Christen, Peter and Hegland, Markus and Nielsen⋆, Ole M. and Roberts, Stephen and Strazdins, Peter and Semenova, Tatiana and Altas, Irfan},
  year = {2001}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers